/ Applied Quantitative Methods
Public Policy 6290-001

Fall 2017

Instructor: / Prof. Daniel L. Carlson
Lecture/Lab: / Mon 6:00 – 9:00p
Classroom: / 340 Alfred Emory Building
Office:
Phone: / 234 Alfred Emory Building
801-587-5664
801-581-6521 (Department phone)
E-mail: /
Office Hours: / Tues/Thurs 12:15-2:15 or by appt.

Course Description:

This course provides a basic introduction to the logic, application, interpretation, and presentation of statistical analysis in the social sciences for independent research and program evaluation. Statistics can be used to simplify and concisely present information and compare differences across groups, assess the effectiveness of policy interventions and programs, and make inferences about populations based on sample data. This course introduces students to exploratory data analysis, probability theory, and both descriptive and inferential statistics. It aims to provide a solid foundation for studying advanced statistics, conducting data analysis, and reporting study results. Students also learn how to use one of the computer programs (SPSS) that is widely used to perform statistical analysis.

Student Accommodations:

Students who wish to request accommodations for a disability may do so by registering with the Center for Disability Services. Students may only be accommodated upon issuance by the Center for Disability Services, of a signed Accommodation Plan and are responsible for providing a copy of that plan to instructors of all classes in which an accommodation is sought.

Course Materials:

·  Software: SPSS and Microsoft Excel

o  To purchase SPSS for your personal compute go here: http://www.onthehub.com/spss/

·  Computing: Access to Canvas; a Flashdrive or Cloud storage (e.g., Box; Dropbox)

·  Other: Scientific Calculator

·  Recommended Textbook: Levin, Jack, James Alan Fox, and David R. Forde. 2014. Elementary Statistics in Social Research, 12th Ed. Boston: Pearson.

Course Requirements:

1. Attendance is one of the primary predictors of scholarly performance. I cannot see any student doing well in this course without attending lecture consistently. Because of this, I take attendance regularly to provide myself with a tool to help evaluate your performance and make decisions about borderline grades. Additionally, while I do provide my lecture slides to students, these slides do not cover all the material presented. Obtaining lecture material and information from announcements is the student’s responsibility -- ALWAYS. If you miss class get notes from another student. Furthermore, some of the material I present is unique to the lecture (i.e., it is not in your textbook). Therefore, the only way to obtain it is to be present.

2. There will be 5 homework assignments. These will include a combination of problem solving (hand and computer calculations), conceptual interpretation, and presentation of results. Together these assignments are worth 70% of your course grade (Each assignment is worth 14% of the course grade)

3. There will be a comprehensive take-home final examination. The final exam is worth 30% of the course grade. The Final Exam is due in my mailbox by 3pm on Friday December 16th. DO NOT EMAIL IT TO ME!

Course Policies

·  Collaboration: I believe that cooperation is important to the learning process and I encourage you to work together on homework assignments, BUT NOT ON THE EXAM. On homework assignments, I leave it up to your individual consciences to determine the fine line between cooperative work and mere copying from one another. In other words, talk and consult with each other as much as you like but in the end each student is required do their own individual written work.

·  Withdrawing from Course: The semester midpoint, [10/20/17] is the last day to withdraw from a full semester class and receive a possible grade of W, except for hardship withdrawal. After the midpoint of the term, voluntary withdrawals cannot occur.

·  Attendance and Excused Absences: If you miss a class, make sure that you get notes from another student. I do understand that some absences may be necessary (if you are very ill for example, please don’t come to class). I ask that you provide documentation for those absences, in advance if at all possible. Absences may be excused for the following reasons: University-sponsored events, government obligations, religious observances, family emergencies, and medical emergencies. I will determine if your emergency and your documentation are sufficient to excuse your absence. Documentation for religious observances and university sponsored events are due by the 1st week of the semester.

NOTE: Grades in this course are weighted. Although I assign points to assignments, your final grade is not based on the cumulative point total earned on assignments. Rather each graded component is given a weight and those weights are summed to determine your final grade.

Grading Scale

A 93-100% / B 83-86% / C 73-76% / D 63-66%
A- 90-92% / B- 80-82% / C- 70-72% / D- 60-62%
B+ 87-89% / C+ 77-79% / D+ 67-69% / E 0-59%

·  Late Assignments: All assignments are to be turned in by you at the beginning of the class period for which they due (see lecture outline for due dates). I do not accept assignments via email or placed in my mailbox. The only time it is acceptable to turn in an assignment via email or to my mailbox is when I have given you my permission. Late assignments will result in a reduction of 2 letter grades (~10% off) for every day (including weekends) it is late. This applies in all cases, except excusable absences (see above list). Things that do not fall under the category of excusable absences/lateness: Your car dying; your printer running out of ink; the computer lab being closed; missing the bus/train; getting stuck out of town, etc. Documentation of circumstances will be required for late work to receive full credit.

Academic Misconduct: A student who engages in academic misconduct as defined in Part I.B. of University Policy 6-400 (http://regulations.utah.edu/academics/6-400.php) may be subject to academic sanctions including but not limited to a grade reduction, failing grade, probation, suspension or dismissal from the program or the University, or revocation of the student's degree or certificate. Sanctions may also include community service, a written reprimand, and/or a written statement of misconduct that can be put into an appropriate record maintained for purposes of the profession or discipline for which the student is preparing. All violations will be formally reported to the Program Director and to the Dean of the College of Social and Behavioral Science.

While many people associate academic misconduct with only "cheating," academic misconduct actually includes a wider scope of student behaviors. “Academic misconduct” includes, but is not limited to, cheating, misrepresenting one's work, inappropriately collaborating, plagiarism, and fabrication or falsification of information, as defined further below. It also includes facilitating academic misconduct by intentionally helping or attempting to help another to commit an act of academic misconduct.

a.  “Cheating” involves the unauthorized possession or use of information, materials, notes, study aids, or other devices in any academic exercise, or the unauthorized communication with another person during such an exercise. Common examples of cheating include, but are not limited to, copying from another student's examination, submitting work for an in-class exam that has been prepared in advance, violating rules governing the administration of exams, having another person take an exam, altering one's work after the work has been returned and before resubmitting it, or violating any rules relating to academic conduct of a course or program.

b.  Misrepresenting one's work includes, but is not limited to, representing material prepared by another as one's own work, or submitting the same work in more than one course without prior permission of both faculty members.

c.  “Plagiarism” means the intentional unacknowledged use or incorporation of any other person's work in, or as a basis for, one's own work offered for academic consideration or credit or for public presentation. Plagiarism includes, but is not limited to, representing as one's own, without attribution, any other individual's words, phrasing, ideas, sequence of ideas, information or any other mode or content of expression.

d.  “Fabrication” or “falsification” includes reporting experiments or measurements or statistical analyses never performed; manipulating or altering data or other manifestations of research to achieve a desired result; falsifying or misrepresenting background information, credentials or other academically relevant information; or selective reporting, including the deliberate suppression of conflicting or unwanted data. It does not include honest error or honest differences in interpretations or judgments of data and/or results.

COURSE OUTLINE
The dates provided here are tentative and could change depending on how this class proceeds. The readings should be completed prior to the date of the class for which they are assigned.
DATE / TOPIC / Readings (Levin, Fox, and Forde) & Due Dates
Aug. 21 / Introduction to Quantitative Data Analysis, SPSS, & Data
Aug. 28 / Principles of Variable Measurement and Data Organization / Ch. 1 and Ch. 2
Variable Measurement and Data Organization in SPSS and MS Excel / HWK 1 OUT
Sept. 4 / Labor Day No Class
Sept. 11 / Descriptive Statistics: Measures of Central Tendency / Ch. 3 and Ch.4
Descriptive Statistics: Measures of Dispersion
Sept. 18 / Descriptive Statistics in SPSS
Probability and the Normal Curve / Ch. 5
Sept. 25 / Probability and the Normal Curve (cont.) / Ch. 6
Populations, Samples, and Confidence Intervals / HWK 1 DUE; HWK 2 OUT
Oct. 2 / Populations, Samples, and Confidence Intervals
Confidence Intervals in SPSS
Oct. 9 / Fall Break No Class
Oct. 16 / Introduction to Statistical Inference & Significance / Ch.7
HWK 2 DUE
Oct. 23 / Comparing Group Means – Independent Samples T-tests / HWK 3 OUT
T-tests in SPSS
Oct. 30 / Analysis of Variance (ANOVA) / Ch. 8
Analysis of Variance (ANOVA) – SPSS
Nov. 6 / Nonparametric Tests of Significance / Ch. 9
Nonparametric Tests – SPSS / HWK 3 DUE; HWK 4 OUT
Nov. 13 / Correlation / Ch. 10 and 12
Correlation – SPSS
Nov. 20 / Introduction to Ordinary Least Squares (OLS) Regression / Ch. 11
OLS Regression – SPSS / HWK 4 DUE; HWK 5 OUT
Nov. 27 / Introduction to Multivariate OLS Regression
Multivariate OLS Regression - SPSS
Dec. 4 / Generalizations of OLS Regression / HWK 5 DUE; Final OUT
Review of Bivariate and Multivariate Regression
Dec. 15 / FINAL DUE by 3pm